Biomaterials and bioelectronics for self-powered neurostimulation
Self-powered neurostimulation via biomaterials and bioelectronics innovation has emerged
as a compelling approach to explore, repair, and modulate neural systems. This review …
as a compelling approach to explore, repair, and modulate neural systems. This review …
Diffbp: Generative diffusion of 3d molecules for target protein binding
Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Most previous works typically generate atoms autoregressively, with element …
drug discovery. Most previous works typically generate atoms autoregressively, with element …
Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of
compound-protein interactions for rational drug discovery. Existing deep learning-based …
compound-protein interactions for rational drug discovery. Existing deep learning-based …
Protein 3d graph structure learning for robust structure-based protein property prediction
Protein structure-based property prediction has emerged as a promising approach for
various biological tasks, such as protein function prediction and sub-cellular location …
various biological tasks, such as protein function prediction and sub-cellular location …
Mape-ppi: Towards effective and efficient protein-protein interaction prediction via microenvironment-aware protein embedding
Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play
a key role in life activities. The growing demand and cost of experimental PPI assays require …
a key role in life activities. The growing demand and cost of experimental PPI assays require …
Evaluating representation learning on the protein structure universe
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation
learning on protein structures with Geometric Graph Neural Networks. We consider large …
learning on protein structures with Geometric Graph Neural Networks. We consider large …
A systematic study of joint representation learning on protein sequences and structures
Learning effective protein representations is critical in a variety of tasks in biology such as
predicting protein functions. Recent sequence representation learning methods based on …
predicting protein functions. Recent sequence representation learning methods based on …
Lightweight contrastive protein structure-sequence transformation
Pretrained protein structure models without labels are crucial foundations for the majority of
protein downstream applications. The conventional structure pretraining methods follow the …
protein downstream applications. The conventional structure pretraining methods follow the …
MMDesign: Multi-Modality Transfer Learning for Generative Protein Design
Protein design involves generating protein sequences based on their corresponding protein
backbones. While deep generative models show promise for learning protein design directly …
backbones. While deep generative models show promise for learning protein design directly …
Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
Protein-protein bindings play a key role in a variety of fundamental biological processes,
and thus predicting the effects of amino acid mutations on protein-protein binding is crucial …
and thus predicting the effects of amino acid mutations on protein-protein binding is crucial …